Data Science with R: Data Analysis and Visualization

Data Science with R: Data Analysis and Visualization

Data Science with R: Data Analysis and Visualization

Beginner

This course is a 35-hour program designed to provide a comprehensive introduction to R. You’ll learn how to load, save, and transform data as well as how to write functions, generate graphs, and fit basic statistical models with data. In addition to a theoretical framework in which you will learn the process of data analysis, this course focuses on the practical tools needed in data analysis and visualization. By the end of the course, you will have mastered the essential skills of processing, manipulating and analyzing data of various types, creating advanced visualizations, generating reports, and documenting your codes.

Course Overview
Beginner

This course is a 35-hour program designed to provide a comprehensive introduction to R. You’ll learn how to load, save, and transform data as well as how to write functions, generate graphs, and fit basic statistical models with data. In addition to a theoretical framework in which you will learn the process of data analysis, this course focuses on the practical tools needed in data analysis and visualization. By the end of the course, you will have mastered the essential skills of processing, manipulating and analyzing data of various types, creating advanced visualizations, generating reports, and documenting your codes.

November Session
$2190.00
November Session
Nov 4 - Dec 16, 2017, 10:00am-5:00pm
January Session
$2190.00
Early bird pricing
$2080.50
January Session
Jan 20 - Feb 24, 2018, 10:00am-5:00pm

Date and Time

November Session

Nov 4 - Dec 16, 2017, 10:00am-5:00pm
Day 1: November 4, 2017
Day 2: November 18, 2017
Day 3: December 2, 2017
Day 4: December 9, 2017
Day 5: December 16, 2017
$2190.00$2080.50
Add to Cart

January Session Early-bird Pricing!

Jan 20 - Feb 24, 2018, 10:00am-5:00pm
Day 1: January 20, 2018
Day 2: January 27, 2018
Day 3: February 3, 2018
Day 4: February 10, 2018
Day 5: February 24, 2018
$2190.00$2080.50
Add to Cart

April Session Early-bird Pricing!

Apr 21 - Jun 2, 2018, 10:00am-5:00pm
Day 1: April 21, 2018
Day 2: April 28, 2018
Day 3: May 5, 2018
Day 4: May 19, 2018
Day 5: June 2, 2018
$2190.00$2080.50
Add to Cart

July Session Early-bird Pricing!

Jul 28 - Aug 25, 2018, 10:00am-5:00pm
Day 1: July 28, 2018
Day 2: August 4, 2018
Day 3: August 11, 2018
Day 4: August 18, 2018
Day 5: August 25, 2018
$2190.00$2080.50
Add to Cart

September Session Early-bird Pricing!

Sep 8 - Oct 13, 2018, 10:00am-5:00pm
Day 1: September 8, 2018
Day 2: September 15, 2018
Day 3: September 22, 2018
Day 4: September 29, 2018
Day 5: October 13, 2018
$2190.00$2080.50
Add to Cart

October Session Early-bird Pricing!

Oct 27 - Dec 8, 2018, 10:00am-5:00pm
Day 1: October 27, 2018
Day 2: November 3, 2018
Day 3: November 17, 2018
Day 4: December 1, 2018
Day 5: December 8, 2018
$2190.00$2080.50
Add to Cart

Instructors

David Romoff
David Romoff
David Romoff is a risk management consultant with 10 years of experience modeling market and credit risk using the latest methods and technologies. David's recent work includes serving as Manager of Risk Management at On Deck Capital, a business lending company in the FinTech space that uses machine learning models to underwrite loans. David was responsible for estimating and reporting losses on the book of loans. Previously, David worked in Enterprise Risk Management at AIG for five years where he designed and supported models on insurance risk, credit risk, and capital allocation. Before AIG, he worked at Bear Stearns in counterparty credit risk. David has an MBA from the Zicklin School of Business in New York City and a Master of Science in Actuarial Science from Columbia University. His undergraduate degree is from the State University of New York at Albany, where he studied psychology and philosophy.
Aaron Wagner
Aaron Wagner
Aaron Wagner holds a MS in biology from the University of Colorado Denver, where he focused on community structure, biodiversity, and pathogen disturbance in high-elevation ecosystems. His published research focuses on the impacts of invasive pathogens on the function of stress-tolerant foundation species in plant communities. Currently he is a Data Scientist for a pension fund in New York City, maintains an R package that interacts with Survey Gizmo APIs, and is co-developing an R package that streamlines interactive maps.

Product Description


Overview

 

This course is a 35-hour program designed to provide a comprehensive introduction to R for Data Analysis and Visualization. You’ll learn how to load, save, and transform data as well as how to write functions, generate graphs, and fit basic statistical models with data. In addition to a theoretical framework in which to understand the process of data analysis, this course focuses on the practical tools needed in data analysis and visualization. By the end of the course, you will have mastered the essential skills of processing, manipulating and analyzing data of various types, creating advanced visualizations, generating reports, and documenting your codes.

Details

 


Prerequisites

 

  • Basic knowledge about computer components
  • Basic knowledge about programming

Syllabus

Unit 1: Basic Programming with R

  • Introduction to R
    • What is R?
    • Why R?
    • How to learn R
    • RStudio, packages, and the workspace
  • Basic R language elements
    • Data object types
    • Local data import/export
    • Introducing functions and control statements
  • In-depth study of data objects
  • Functions
  • Functional Programming

Unit 2: Basic Data Elements

  • Data transformation
    • Reshape
    • Split
    • Combine
  • Character manipulation
  • String manipulation
  • Dates and timestamps
  • Web data capture
  • API data sources
  • Connecting to an external database

Unit 3: Manipulating Data with “dplyr”

  • Subset, transform, and reorder datasets
  • Join datasets
  • Groupwise operations on datasets

Unit 4: Data Graphics and Data Visualization

  • Core ideas of data graphics and data visualization
  • R graphics engines
    • Base
    • Grid
    • Lattice
    • ggplot2
  • Big data graphics with ggplot2

Unit 5: Advanced Visualization

  • Customized graphics with ggplot2
    • Titles
    • Coordinate systems
    • Scales
    • Themes
    • Axis labels
    • Legends
  • Other plotting cases
    • Violin Plots
    • Pie charts
    • Mosaic plots
    • Hierarchical tree diagrams
    • scatter plots with multidimensional data
    • Time-series visualizations
    • Maps
    • R and interactive visualizations

Final Project

After 35 hours of structured lectures, students are encouraged to work on an exploratory data analysis project based on their own interests. A project presentation demo will be arranged afterwards.


Recommended Readings

 

  • R in a Nutshell, by Joseph Adler
  • R Graphics Cookbook: http://www.cookbook-r.com/Graphs/
  • Data Manipulation with R, by Phil Spector

Reviews

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Be the first to review “Data Science with R: Data Analysis and Visualization”

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Instructors

David Romoff
David Romoff
David Romoff is a risk management consultant with 10 years of experience modeling market and credit risk using the latest methods and technologies. David's recent work includes serving as Manager of Risk Management at On Deck Capital, a business lending company in the FinTech space that uses machine learning models to underwrite loans. David was responsible for estimating and reporting losses on the book of loans. Previously, David worked in Enterprise Risk Management at AIG for five years where he designed and supported models on insurance risk, credit risk, and capital allocation. Before AIG, he worked at Bear Stearns in counterparty credit risk. David has an MBA from the Zicklin School of Business in New York City and a Master of Science in Actuarial Science from Columbia University. His undergraduate degree is from the State University of New York at Albany, where he studied psychology and philosophy.
Aaron Wagner
Aaron Wagner
Aaron Wagner holds a MS in biology from the University of Colorado Denver, where he focused on community structure, biodiversity, and pathogen disturbance in high-elevation ecosystems. His published research focuses on the impacts of invasive pathogens on the function of stress-tolerant foundation species in plant communities. Currently he is a Data Scientist for a pension fund in New York City, maintains an R package that interacts with Survey Gizmo APIs, and is co-developing an R package that streamlines interactive maps.

Product Description


Overview

 

This course is a 35-hour program designed to provide a comprehensive introduction to R for Data Analysis and Visualization. You’ll learn how to load, save, and transform data as well as how to write functions, generate graphs, and fit basic statistical models with data. In addition to a theoretical framework in which to understand the process of data analysis, this course focuses on the practical tools needed in data analysis and visualization. By the end of the course, you will have mastered the essential skills of processing, manipulating and analyzing data of various types, creating advanced visualizations, generating reports, and documenting your codes.

Details

 


Prerequisites

 

  • Basic knowledge about computer components
  • Basic knowledge about programming

Syllabus

Unit 1: Basic Programming with R

  • Introduction to R
    • What is R?
    • Why R?
    • How to learn R
    • RStudio, packages, and the workspace
  • Basic R language elements
    • Data object types
    • Local data import/export
    • Introducing functions and control statements
  • In-depth study of data objects
  • Functions
  • Functional Programming

Unit 2: Basic Data Elements

  • Data transformation
    • Reshape
    • Split
    • Combine
  • Character manipulation
  • String manipulation
  • Dates and timestamps
  • Web data capture
  • API data sources
  • Connecting to an external database

Unit 3: Manipulating Data with “dplyr”

  • Subset, transform, and reorder datasets
  • Join datasets
  • Groupwise operations on datasets

Unit 4: Data Graphics and Data Visualization

  • Core ideas of data graphics and data visualization
  • R graphics engines
    • Base
    • Grid
    • Lattice
    • ggplot2
  • Big data graphics with ggplot2

Unit 5: Advanced Visualization

  • Customized graphics with ggplot2
    • Titles
    • Coordinate systems
    • Scales
    • Themes
    • Axis labels
    • Legends
  • Other plotting cases
    • Violin Plots
    • Pie charts
    • Mosaic plots
    • Hierarchical tree diagrams
    • scatter plots with multidimensional data
    • Time-series visualizations
    • Maps
    • R and interactive visualizations

Final Project

After 35 hours of structured lectures, students are encouraged to work on an exploratory data analysis project based on their own interests. A project presentation demo will be arranged afterwards.


Recommended Readings

 

  • R in a Nutshell, by Joseph Adler
  • R Graphics Cookbook: http://www.cookbook-r.com/Graphs/
  • Data Manipulation with R, by Phil Spector

Reviews

There are no reviews yet.

Be the first to review “Data Science with R: Data Analysis and Visualization”

Your email address will not be published. Required fields are marked *

Testimonials View All Student Testimonials

Joe Keepers
Joe Keepers
This course was a masterpiece. Derek Darves the instructor, quickly brought us to competency with the R programming language. Then he expanded the course by introducing the packages used for analysis and visualization, progressing through introductory use to somewhat elegant and sophisticated programming challenges. Ultimately Derek brought us to a self-sufficiency level for continuing our R education. The course was a pleasure as Derek is clearly an R expert and aficionado weaving many practical tips and historical insights into the lectures. His programming experience, statistical insights and extensions of the course materials gave it a graduate level feel, while never ignoring the fundamental skills being taught. I highly recommend it.
Carlos S.
Carlos S.
I attended NYCDSA 5-week course (5 full-time days, one per week) in 4Q16 as part of my preparation to start the same school bootcamp. This was a great introductory start to learn R due to the comprehensive syllabus and dedicated teacher effort. About the syllabus, you will learn Base R syntax and principal data structures identification and manipulation plus a bunch of other packages (e.g. DPLYR) that will make your life easier when treating data sets. The instructor was a Sr. Data Scientist that really gave us two sides: theoretical and hands-on day-to-day professional experience views. This was very helpful. I found that there're lots of courses out there but most of the times taught by recently-graduated teachers that haven't applied a lot of the syllabus to real-life professional situations. This for me was a plus. The only soft point of the course was that I would have liked to go more deeper into web scrapping, yet it's true this course name is 'data analysis and visualization in R' and not 'Web scrapping using R'. One advice only for prospect students: block your agendas during five weeks since you will need a lot of time to review the materials and deliver exercises, which after all it's not a bad thing as it makes you feel good as you feel you have learned a lot. Highly recommended.
Vinod Shekar
Vinod Shekar
This was a great class that I truly enjoyed attending every Saturday for 5 weeks. The class had a pretty steep learning curve but the slides and the homeworks did a good job of teaching the material. Our instructor, Derek, was an R guru and could answer any question we threw at him. I definitely plan to continue learning R and I can attribute my enthusiasm to having taken this class.
Sandra Barral
Sandra Barral
Great course to get started with R programming. Convenient location in the city, nice classroom mates with very different backgrounds, and an amazing instructor, Derek has an impressive deep knowledge of R and he is a very talented and dynamic teacher Totally recommended to gain beginner understanding of this language.
Ethan Weber
Ethan Weber
In October, I signed up for the 12 week bootcamp which starts in January. They recommended I take this course (free of charge) in preparation for the bootcamp to prepare myself in the language (I'm already comfortable in Python). I'm giving this course 5 stars because, for the format, I think they did a perfect job. The instructor, Derek Darves, was definitely qualified and a nice guy in general. They gave the tools to learn the basics of data analysis, manipulation and visualization.
Iris Huang
Iris Huang
AVPt, Quantitative Analytics at
Barclays Investment Bank
I really enjoy taking the R course with Amy Ma. She's patient and thorough. Her analogies made it easier  for me to understand the R syntax. I really like the in-class coding exercise and it was good to practice what I have learned. Amy's class is very interactive. She doesn't just talk off the slides. She always codes with us and shows us different ways of doing the same thing or breaking down the code part by part. She would compare and contrast the nuances between different commands, which was quite helpful.
Jiaqi Luo
Jiaqi Luo
I really enjoyed the R course with instructor Amy Ma. The content is very practical. It can be directly applied to solve real-world data analysis problem. We had many in-class coding exercises, which helped us understand the R syntax. Also, Amy tried her best to provide a lot of useful resources. We could tell that she is very passionate about what she is doing, and she is patient with students. We could reach her after class through email, even the course was finished. I highly recommend this course to anyone who is interested in data analysis and wants to learn R from the beginning.
Jinying Li
Jinying Li
Director of Campaign Analytics at
Bloomberg LP

Vivian came to our company and taught us five one-day sessions in R from entry level to intermediate level. I had no experience in R before. But I have learned a lot from Vivian and from the resources she provided, both from online and from the books. The homework and the office hour are also very helpful. After the classes, I have started to use R in my job from very basic stuff to more advanced data manipulation and analysis. Vivian is very knowledgeable in R and a warm person to work with. Thank you, Vivian.

James Cai
James Cai
Head of Data Science at
Roche

Vivian led two training sessions for our team at our company location, and covered both introductory and intermediate data analysis using R in five days. We very much enjoyed Vivian’s engaging teaching style and the hands-on exercises. She was able to draw on a broad array of real world experiences she had with clients in many different industries. This helped us feel the excitement of how data science techniques were used to solve challenging problems. I particularly like the fact that she insisted on the post-training projects that were completed by all attendees. It was satisfying for me to see what our team could do in a very short period of time using the skills gained in the training.

Harrison Adler
Harrison Adler
Product Adoption/Data Scientist at
Google

I took both the intensive beginner and intensive intermediate R classes back-to-back on weekends over a four-month period. Although 7 hours a session may feel hefty, once you’re in the class time will fly by. Sessions split time between lecture and hands-on exercises, so you have lots of time to ask questions. Homework assignments are manageable – Vivian is very accessible should you have a question by email or in-person office hours. Each class ends with a Demo Day of a project of your choice. You will access real data from the Internet using APIs and analyze this information and ways you never would have thought possible using Excel or even SQL! Because of the initiative I took to learn the material, I have accepted a position as a Data Scientist at Google. A big thank you to Vivian and the team at the NYC DSA for helping me make the leap in my career from business analyst to data scientist!

Akiko Togami
Akiko Togami
Business Analytics | Data Visualization at
Bloomberg LP
Before taking Vivian’s R Intensive beginner course, I had no experience in programming and I was just someone who was interested in data visualization in more sophisticated ways than making bar graphs in Excel. The class was really intense. A lot of preview and review would be needed to keep up with the 5 weeks long course, but Vivian always lifted our spirits up and consistently provided necessary help, online and offline, literally anytime we needed. After completion of the class, I am still not used to myself who can confidently use various packages and functions to come up with different kinds of data visualization and manipulation. This is indescribably great feeling and Vivian’s voice “delivery is our goal” echoes in my head now. Creation is pure joy (though the process could be painful). I think taking this course was one of the best investments I made in my life, and it could not be it if that was not brought by you, Vivian. Thank you so much!!
Roger Huang
Roger Huang
Data Analyst at
Shyp

Very informative class. Vivian uses intensive exercises and hand-on practices to make sure you understand how to use the packages she teaches!

Marifel Corpuz
Marifel Corpuz
Associate Director, Advanced Analytics at
MEC Global, Rosetta

I completed the Intensive beginner course for R and I highly recommend it! I’ve learned a lot in 5 weeks and I can say that I am now an R convert (from SAS). I’ve learned so many functions and packages that I am now able to use them confidently at work. Vivian was also a great, hard working teacher who encouraged every one in the class to study harder which means she really cared that that her students would become great data scientists sooner than later. I like the class so much I am now taking the R intermediate class.

Donald Fleurantin
Donald Fleurantin
US Private Equity Lead at
Thomson Reuters
NYC Data Science Academy provided me great exposure to data science topics that I haven’t come across in either school or previous jobs. The hands-on assignments are practical and make use of real-world examples. As product development is becoming more data-driven, it will be crucial for product teams to have a solid grasp of data analysis which NYC Data Science Academy fills the knowledge/skill gap.
Mike Selender
Mike Selender
Technical Analyst at
Chubb

I took the initial version of this class late fall 2013 and found it to be well worth the time. The slides, examples and exercises were well organized. Scott Kostyshak’s presentation style is clear and concise. The second iteration will have twice the classroom hours and cover a lot of material that there wasn’t time for in the initial format. It’s worth the investment if you want to dive into the R ecosystem.

Annaliese Wiederspahn
Annaliese Wiederspahn
Managing Member at
Equipoise

Super helpful. Vivian is a fantastic teacher. She really pushes everyone to dig in and start solving problems.

Date and Time

November Session

Nov 4 - Dec 16, 2017, 10:00am-5:00pm
Day 1: November 4, 2017
Day 2: November 18, 2017
Day 3: December 2, 2017
Day 4: December 9, 2017
Day 5: December 16, 2017
$2190.00
Add to Cart

January Session Early-bird Pricing!

Jan 20 - Feb 24, 2018, 10:00am-5:00pm
Day 1: January 20, 2018
Day 2: January 27, 2018
Day 3: February 3, 2018
Day 4: February 10, 2018
Day 5: February 24, 2018
$2190.00$2080.50
Add to Cart

April Session Early-bird Pricing!

Apr 21 - Jun 2, 2018, 10:00am-5:00pm
Day 1: April 21, 2018
Day 2: April 28, 2018
Day 3: May 5, 2018
Day 4: May 19, 2018
Day 5: June 2, 2018
$2190.00$2080.50
Add to Cart

July Session Early-bird Pricing!

Jul 28 - Aug 25, 2018, 10:00am-5:00pm
Day 1: July 28, 2018
Day 2: August 4, 2018
Day 3: August 11, 2018
Day 4: August 18, 2018
Day 5: August 25, 2018
$2190.00$2080.50
Add to Cart

September Session Early-bird Pricing!

Sep 8 - Oct 13, 2018, 10:00am-5:00pm
Day 1: September 8, 2018
Day 2: September 15, 2018
Day 3: September 22, 2018
Day 4: September 29, 2018
Day 5: October 13, 2018
$2190.00$2080.50
Add to Cart

October Session Early-bird Pricing!

Oct 27 - Dec 8, 2018, 10:00am-5:00pm
Day 1: October 27, 2018
Day 2: November 3, 2018
Day 3: November 17, 2018
Day 4: December 1, 2018
Day 5: December 8, 2018
$2190.00$2080.50
Add to Cart